Three Essential Rules to Achieve a Great ROI with AI

Think of AI implementation as investing in an expensive, high-performance oven for your restaurant.

As cool as it may be, an oven is basically just a tool. To offer great cuisine, you still need good-quality products, fantastic chefs, and a list of recipes to cook properly.

The same goes for AI, which should be combined with three additional elements in order to boost your company’s performance and achieve a satisfying return on investment (ROI):

1. Data: the products that your AI system will “cook” and transform into precious insights.
2. Experienced professionals: a team of “chefs” with the necessary know-how to get the best from your AI solution.
3. The right strategy: once you have proper data and an exceptional team, you still need to know how and when to use AI in the right way. That’s your recipe!

 

The ROI of AI: potential and requirements

Nowadays, there is no industry that gives up on the valuable contribution of artificial intelligence.

From energy to the mining sector, from education to human resources, AI permeates virtually any business, generally ensuring a positive ROI from its implementation.

The return on investment in the AI field can come in four different ways:

• Increased revenues, for example with product recommendation in digital stores
• Cost reduction and improved efficiency by automating business processes
• Risk management, which means using AI for cybersecurity or personnel safety
• Non-Financial factors, such as customer care’s quality and speed improvements

The best sectors in terms of ROI

According to research by ESI ThoughtLab and Deloitte, the top areas in terms of returns from AI investments include customer care (74%), IT operations and infrastructures (69%), planning and decision-making (66%).

While such data is promising, many companies are still unable to achieve satisfactory ROI from their AI projects.

That’s because some basic requirements are needed to get positive results.

“The top areas in terms of returns from AI investments include customer care (74%), IT operations and infrastructures (69%), planning and decision-making (66%).”

Which companies can succeed?

Based on the ESI ThoughtLab survey, all companies achieving high ROI (over 5%) had implemented key practices in data management, results tracking, and security.

Another factor to consider is the companies’ experience and maturity: leading businesses can boast an average of a 4.3% ROI and a relatively fast paycheck period (1.2 years) for their AI projects, compared to the 0.2% ROI and a longer payback period (1.6 years) of beginners.

To sum it up, what companies investing in artificial intelligence should understand first is that AI implementation can only be leveraged in a profitable way with proper preparation, consisting of good data, strategies, and professionals, as we mentioned in our introduction.

Regarding this, let’s delve a little bit more!

 

1. Achieving ROI with good data

AI is an incredible tool, which is literally reforging our way of living. That’s especially true when we consider its most recent and powerful branches, namely machine learning and deep learning.

The potential of ML lies in its ability to autonomously recognize the key relationships between raw data and create forecast models.

Such superpower can be applied basically anywhere: from marketing, in which ML-based systems scan customer data to personalize ads, to power utilities, which use AI to predict the energy load based on weather conditions.

 

AI can be really demanding

The fact of being so data-hungry is also AI’s biggest limitation when it comes to leveraging it.

AI and ML cannot generate useful insights from any information you feed them with. Data must be suitable in terms of both quantity and quality.

Regarding the quantitative aspect, there are several tricks to increase our information menu. Our technical manager Konstantin talked about it in this article.

 

What we need is high-quality data

Moving on to the quality of the data, the situation becomes much more tricky. Our dataset should always be broadly representative and multifaceted, taking into account the numerous conditions under which our ML model can be applied.

This means, for example, that if we are preparing a set containing the information of our customers, it is necessary to consider numerous aspects: personal data, client preferences, potential churn and retention rates, willingness to buy, and so on.

Once this information is gathered, any company that deploys AI can use it to guide each customer interaction.

All of this is easier said than done, because collecting the right data takes time and money, and the risk of compromising or corrupting our dataset with incorrect, biased, or partial information is always around the corner.

 

A pair of examples

This is particularly evident in speech analysis and the tools that rely on it, such as chatbots. If you train them with only formal texts (e.g. newspaper articles and official documentation) they probably won’t be able to understand the typically informal language of standard users.

And what about systems based on artificial intelligence for the maintenance of industrial machines?

If data collected is not representative of the plant’s operations (eg: sensors keep on running when the machine is off), the AI will have some trouble recognizing between standard and non-standard behaviors and, consequently, understanding if something is going wrong.

Data is the fuel… and the limit

Problems regarding data availability are quite common and represent a relevant reason why companies struggle to implement AI in their business, as research has shown.

For example, according to a survey released in 2019 by the Pistoia Alliance, insufficient access to data is one of the biggest barriers to the adoption of AI for 52% of respondents.

 

2. Achieving ROI with great professionals

Staff training is a key aspect of any successful business, even more so when it comes to managing a powerful and complex technology like AI.

And if you disagree with this statement… “Houston, we have a problem”.

According to the 2nd Edition of Deloitte’s State of AI in the Enterprise, 69% of companies are facing a moderate, major, or extreme skills gap when deploying AI.

This shows that getting the full benefit from AI can be extremely challenging when your staff is not prepared.

“69% of companies are facing a moderate, major, or extreme skills gap when deploying AI.”

How to prepare your team

When a company introduces AI and ML technologies into a given domain, it is necessary to educate and involve all professionals who regularly participate in all related operational processes.

We are therefore not just talking about AI systems specialists, but also engineers, researchers, plant operators, and so on.

These employees are perfectly familiar with the environment in which AI will be implemented. Therefore, they can understand the context from which the data will be extracted and, subsequently, reprocessed into models and leveraged.

 

What about new recruits?

Of course, the retraining and relocation of your employees shall be performed along with the recruitment of new professionals (in-house or outsourcing) for the necessary specialistic categories, for example, data scientists.

Such AI experts should not be regarded as a separate group from the rest of the employees.

Rather, your AI specialists will be combined with the operational teams to train non-technical colleagues on the new role of artificial intelligence within business operations.

3. Achieving ROI with the best strategies

The previous point concerning staff reorganization brings us to the last requirement of our list: wise planning based on the right strategies.

Indeed, one of these strategies concerns all the staff readjustments necessary for a correct AI implementation. Ensuring the operationalization of AI and good ROI requires a radical restructuring of staff and business processes.

It’s not enough to hire new recruits and throw them into the fray like soldiers in Stalingrad. Rather, different specialists should be able to interact and collaborate in cross-functional teams to catalyze their training on new processes and operations.

 

Why do you need AI?

Another factor to consider besides the company staff is your purpose. Why are you investing in AI systems? I mean, artificial intelligence is not exactly a cheap technology …

Such investment makes sense if you need it to reach a specific goal, and to know if it’s worth it there are adequate tools such as KPIs and benchmarks.

Missing your own company mission and skipping this evaluation phase is definitely a bad strategy. Don’t do it!

Instead, think about how and if AI will help you, but also what kind of AI you need among the five main types: text, visual, interactive, analytic, and functional. We talked about it in this article.

 

Introducing AI is an overall process

I would like to conclude this overview regarding strategies with a couple of observations.

First, AI deployment in your company should not only involve ALL your staff, but also ALL business processes.

If we introduce AI-based automation tools into a specific domain but leave other parts of our organizational structure behind, we risk generating unpleasant bottlenecks.

Think about what can happen, for example, if you automate some marketing processes (sending promotional emails with the rate of fire of a Gatling gun) but continue to manage inventory, orders, and accounting without AI and with maddening slowness.

 

Be prepared for a long-term evolution

Second, when you finally master the AI and the adaptation process is done, the adventure has just begun! That’s the moment in which you should understand how AI can help offer new products or services, and leverage such a powerful tool to develop them.

This will require additional investments in order to sustain your growth, not only in AI tech but also in product management and marketing.

If your evaluations and strategies are correct, you’ll have a positive cascade effect that will further increase your ROI.

And remember: use part of your returns to buy a few bottles of prosecco for your employees. Don’t be greedy.

 

ROI in AI should not be taken for granted

The potential of AI in terms of earnings is undoubtedly massive but, as you have surely understood, it can be fully unlocked as long as you know how to manage it wisely.

We are talking about a technology that requires significant investments and additional requirements in terms of know-how.

Many companies undertake this high-risk venture without knowing what they will face. The result? A large portion of AI initiatives is doomed to fail.

 

What the research says

In fact, according to the Artificial Intelligence Global Executive Study and Research Project of 2019, 7 out of 10 companies in the survey declared minimal or no impact from AI implementation.

Among the companies that made some investment in AI (90% of the total), less than 4 out of 10 reported business benefits from AI in the last three years.

The slice increases to 6 out of 10 if we count only the companies that made significant investments in AI.

 

Get the benefits and dodge the risks

What does it all mean? Well, Investing in AI can guarantee a huge boost to any business, but it also involves some risk that should never be underestimated.

I hope this article will help you make the necessary assessments wisely. Remember the first example of the restaurant, and if you have one, invite the Apro team for dinner.

We like free food.